Peadar Coyle is a Data Analytics professional based in Luxembourg. His intellectual background is in Mathematics and Physics, and he currently works for Vodafone in one of their Supply Chain teams.He is passionate about data science and the lead author of this project. He also contributes to Open Source projects and speaks at EuroSciPy, PyData and PyCon. His expertise is largely in the statistical side of Data Science.
Peadar was asked by various of his interviewees to share his own interview, so he does humbly.
Follow Peadar’s series of interviews with data scientists here.
What projects have you worked on that you wish you could go back to and do better?
I agree that it is better to look forward rather than look backward. And my skills have frankly improved since I first started doing what we could call professional data analysis (which was probably just before starting my Masters a few years ago).
One project I did which springs to mind (and not naming names) is where there was a huge breakdown in communication and misaligned incentives. There needed to be more communication on that project and it overran the initial allotted time. I also spent not enough time communicating up front the risks and opportunities with the stakeholders.
The data was a lot messier than expected, and management had committed to delivered results in 2 weeks. This was impossible, the data cleaning and exploration phase took too long. Now I would focus on quicker wins. I also rushed to the ‘modelling’ phase without really understanding the data. I think such terms ‘understanding the data’ sound a bit academic to some stakeholders, but you need to clearly and articulately explain how important the data generation process is, and the uncertainty in that data.
Some of this comes from experience – now I focus on adding value as quickly as possible and keeping things simple. There I fell to the siren call of ‘do more analysis’ rather than thinking about how the analysis is conveyed.
What advice do you have to younger analytics professionals and in particular PhD students in the Sciences?
I don’t have a PhD but I have recently been giving advice to people in that situation.
My advice is that having a portfolio of work if possible is great, or at least move towards doing an online course on Machine Learning or something cool like that.
The PyData videos are a good start too to watch. I’d recommend if you can to do any outreach or communication skills courses. There are many such courses at a lot of universities around the world, it’ll just help you understand the needs of others.
I think frankly that the most important skill for a Data Science is the ‘tactical application of empathy’ and that is something that working in a team really helps you develop. One thing I feel my Masters let me down on – as is common in Pure Mathematics – was a shortage of experience working in a team.
What do you wish you knew earlier about being a data scientist?
The focus on communication skills, the need to add value every day. The fact that budget or a project can be terminated at any moment.
Adding value every day means showing results and sharing them, talking to people about stuff. Share visualizations, and share results – a lot of data science is about relationships and empathy. In fact I think that the tactical application of empathy is the greatest skill of our times.
[bctt tweet=”‘The tactical application of empathy is the greatest skill of our times’ – @springcoil”]
You need to get out there and speak to the domain specialist, and understand what they understand. I believe that the best algorithms incorporate human as well as machine intelligence.
How do you respond when you hear the phrase ‘big data’?
I do like the distinction of small, medium and big data. I don’t worry so much about the terminology, and I focus on understanding exactly what my stakeholder wants from it.
I think, though, that it is often a distraction. I did one proof of concept as a consultant, that was an operational disaster. We didn’t have the resources to support a dev ops culture, nor did we have the capabilities to support a Hadoop cluster. Even worse the problem really could be solved more intelligently by being in RAM. But I got excited by the new tools, without understanding what they were really for.
I think this is a challenge, part of myself maturing as an engineer/data scientist is appreciating the limits of tools and avoiding the hype. Most companies don’t need a cluster, and the mean size of a cluster will remain one for a long time. Don’t believe the salesmen, and ask the experts in your community about what is needed.
In short: I feel it is strongly misleading but it is certainly here to stay.
How did you end up being a data analyst? What is the most exciting thing about your field?
My academic and professional career have a bit of weird path. I started at Bristol in a Physics and Philosophy program. It was a really exciting time, and I learned a lot (some of it non-academic). I went into that program because I wanted to learn everything. At various points – especially in 2009-2010 the terminology of ‘data science’ began to pick up, and when I went into grad school in 2010, I was ‘aware’ of the discipline. I took a lot of financial maths classes at Luxembourg, just to keep that option open, yet I still in my heart wanted to be an academic.
I eventually realized (after some soul-searching) that academic opportunities were going to be too difficult to get, and that I could earn more in industry. So I did a few industrial internships including one at import.io, and towards the end of my Masters – I did a 6 month internship at a ‘small’ e-commerce company called Amazon.
I learned a lot at Amazon, and it was there that I realized i needed to work a lot harder on my software engineering skills. I’ve been working on them in my working life and through contributing to open source software and my various speaking engagements. I strongly recommend to any wanna data geeks to come to these and share your own knowledge 🙂
The most exciting thing about my field relates to the first statement about physics and philosophy – we truly are drowning in data, and we really with the computational resources we have now have the ability to answer or simulate certain questions in a business context. The web is a microscope, and your ERP system tells you more about your business than you can actually imagine – I’m very excited to help companies exploit their data.
How do you go about framing a data problem – in particular, how do you avoid spending too long, how do you manage expectations etc. How do you know what is good enough?
I like the OSEMIC framework (which I developed myself) and the CoNVO framework (which comes from Thinking with Data by Max Schron – I recommend the following video for an intro and the book itself.)
Let me explain – at the beginning of an ‘engagement’ I look for the Context, Need, Vision and Outcome of the project. Outcome means the delivery and asking these questions by having a conversation with stakeholders is a really good way to get to solving the ‘business problem’.
[bctt tweet=”‘I look for the Context, Need, Vision and Outcome of the project” – @springcoil #datascience”]
A lot of this after a few years in the business still feels like an art rather than a science.
I like explaining to people the Data Science process – obtain data, scrub data, explore, model, interpret and communicate.
I think a lot of people get these kinds of notions and a lot of my conversations recently at work have been about data quality – and data quality really needs domain knowledge. It is amazing how easy it is to misinterpret a number – especially around things like unit conversion etc.
You spent sometime as a Consultant in Data Analytics. How did you manage cultural challenges, dealing with stakeholders and executives? What advice do you have for new starters about this?
I would see a lot of the stuff above. One challenge is that some places aren’t ready for a data scientist nor do they know how to use one. I would avoid such places, and look for work elsewhere.
Some of this is a lack of vision, and one reason I do a lot of talks is to do ‘educated selling’ about the gospel of data-informed decision making and how the new tools such as the PyData stack and R are helping us extract more and more value out of data.
I’ve also found that visualizations help a lot, humans react to stories and pictures more than to numbers.
My advice to new-starters is over communicate, and learn some soft skills. The frameworks I mentioned help a bit in structuring and explaining a project to stakeholders. I recommend also reading this interview series, I learned a lot from it too. 🙂
(image credit: USASOC News Service, CC2.0)
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